ginkgo-cloud-lab

Submit and manage protocols on Ginkgo Bioworks Cloud Lab (cloud.ginkgo.bio), a web-based interface for autonomous lab execution on Reconfigurable Automation Carts (RACs). Use when the user wants to run protein expression and purification (cell-free, E. coli, or Pichia), HiBiT or A280 or LabChip quantification, IVT mRNA/circRNA synthesis, thermal shift / developability assays, Echo-MS enzyme or analyte methods, SPR target onboarding, fluorescent pixel art, or otherwise interact with Ginkgo Cloud Lab services. Covers protocol selection, input preparation, pricing, and ordering workflows.

Quality

93%

Does it follow best practices?

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Securityby

Low

Low-risk findings worth noting

The canonical home for this skill is ginkgo-cloud-lab in K-Dense-AI/scientific-agent-skills

Quality

Content

86%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is well-structured and token-efficient, using compact tables and a decision tree to convey a broad protocol catalog while deferring detail to one-level-deep reference files. Actionability and workflow clarity are strong but slightly held back by high-level ordering steps and an implicit rather than explicit validation/review checkpoint.

Suggestions

Make the ordering workflow's validation loop explicit, e.g. add a step: 'Review the returned feasibility report and price quote; if requirements are unmet, revise parameters/inputs and resubmit before finalizing the order.'

Tighten actionability by noting where each ordering step is performed in the UI (e.g. which page/tab handles parameter configuration and template upload) so the guidance is closer to copy-paste-followable.

Clarify the authentication flow beyond 'account creation or institutional access may be required' (e.g. whether SSO/institutional login is offered or a waitlist applies) so users know what to expect before reaching the protocol catalog.

Dimension	Reasoning	Score
Conciseness	The body is lean and table-driven with no over-explanation of concepts Claude already knows; the instrument inventory and citation procedure are genuine domain specifics that earn their tokens, matching the lean-and-efficient anchor.	5 / 5
Actionability	Concrete actionable guidance for an instruction-only web-UI skill (specific URLs, file formats, prices, a decision tree, and per-protocol reference links), but ordering steps such as "Configure parameters" and "Add any special requirements" stay high-level without exact UI navigation; sits just below the fully-executable anchor.	4 / 5
Workflow Clarity	A clear 5-step ordering sequence is present, and the platform-returned feasibility report and price quote serve as an implicit validation gate so the destructive/batch cap at 3 does not apply; the explicit "review the feasibility report, revise and resubmit if needed" checkpoint is not spelled out, keeping it just below the score-5 anchor.	4 / 5
Progressive Disclosure	The SKILL.md is a concise overview that links to 17 real, one-level-deep reference files (all present and correctly referenced) via well-signaled tables, with clear navigation guidance ("Pick a protocol below, then read its reference file"), matching the clear-overview one-level-deep anchor.	5 / 5
	Total	18 / 20 Passed

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is exemplary: it states concrete capabilities, provides comprehensive natural trigger terms covering the full protocol catalog, and explicitly pairs a what-statement with a use-when clause in a distinctive niche. Voice is third-person/standard trigger form, incurring no penalty.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions ("Submit and manage protocols", "run protein expression and purification", "HiBiT or A280 or LabChip quantification", "IVT mRNA/circRNA synthesis", "thermal shift / developability assays", "Echo-MS enzyme or analyte methods", "SPR target onboarding", "fluorescent pixel art") with comprehensive coverage of the catalog, matching the score-5 anchor; no neighboring anchor fits better since coverage is both specific and broad.	5 / 5
Completeness	Explicitly answers both what ("Submit and manage protocols ... Covers protocol selection, input preparation, pricing, and ordering workflows") and when ("Use when the user wants to run ... or otherwise interact with Ginkgo Cloud Lab services") with concrete trigger phrases, matching the score-5 anchor.	5 / 5
Trigger Term Quality	Comprehensive natural domain terms users would actually name, including synonyms across expression hosts (cell-free, E. coli, Pichia) and quantification readouts (HiBiT, A280, LabChip), plus named assays (IVT, thermal shift, Echo-MS, SPR); matches the comprehensive-coverage anchor.	5 / 5
Distinctiveness Conflict Risk	A clear niche (Ginkgo Cloud Lab on cloud.ginkgo.bio with named protocols and instruments) with distinct triggers and minimal overlap risk with other skills; matches the clear-niche anchor.	5 / 5
	Total	20 / 20 Passed

Validation

93%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: synthetic-sciences/openscience
Path: backend/cli/skills/biology/ginkgo-cloud-lab/SKILL.md
Commit: 4082a2e

Reviewed: 1 day ago

Table of Contents

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